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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Revisiting output coding for sequential supervised learning /

Hao, Guohua. January 1900 (has links)
Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 38-40). Also available on the World Wide Web.

Support vector classification analysis of resting state functional connectivity fMRI

Craddock, Richard Cameron. January 2009 (has links)
Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Parameter incremental learning algorithm for neural networks

Wan, Sheng, January 1900 (has links)
Thesis (Ph. D.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains x, 97 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 81-83).

Graph based semi-supervised learning in computer vision

Huang, Ning, January 2009 (has links)
Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Biomedical Engineering." Includes bibliographical references (p. 54-55).

Kernel methods in supervised and unsupervised learning /

Tsang, Wai-Hung. January 2003 (has links)
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.

Arbres de décision et forêts aléatoires pour variables groupées / Decisions trees and random forests for grouped variables

Poterie, Audrey 18 October 2018 (has links)
Dans de nombreux problèmes en apprentissage supervisé, les entrées ont une structure de groupes connue et/ou clairement identifiable. Dans ce contexte, l'élaboration d'une règle de prédiction utilisant les groupes plutôt que les variables individuelles peut être plus pertinente tant au niveau des performances prédictives que de l'interprétation. L'objectif de la thèse est de développer des méthodes par arbres adaptées aux variables groupées. Nous proposons deux approches qui utilisent la structure groupée des variables pour construire des arbres de décisions. La première méthode permet de construire des arbres binaires en classification. Une coupure est définie par le choix d'un groupe et d'une combinaison linéaire des variables du dit groupe. La seconde approche, qui peut être utilisée en régression et en classification, construit un arbre non-binaire dans lequel chaque coupure est un arbre binaire. Ces deux approches construisent un arbre maximal qui est ensuite élagué. Nous proposons pour cela deux stratégies d'élagage dont une est une généralisation du minimal cost-complexity pruning. Les arbres de décision étant instables, nous introduisons une méthode de forêts aléatoires pour variables groupées. Outre l'aspect prédiction, ces méthodes peuvent aussi être utilisées pour faire de la sélection de groupes grâce à l'introduction d'indices d'importance des groupes. Ce travail est complété par une partie indépendante dans laquelle nous nous plaçons dans un cadre d'apprentissage non supervisé. Nous introduisons un nouvel algorithme de clustering. Sous des hypothèses classiques, nous obtenons des vitesses de convergence pour le risque de clustering de l'algorithme proposé. / In many problems in supervised learning, inputs have a known and/or obvious group structure. In this context, elaborating a prediction rule that takes into account the group structure can be more relevant than using an approach based only on the individual variables for both prediction accuracy and interpretation. The goal of this thesis is to develop some tree-based methods adapted to grouped variables. Here, we propose two new tree-based approaches which use the group structure to build decision trees. The first approach allows to build binary decision trees for classification problems. A split of a node is defined according to the choice of both a splitting group and a linear combination of the inputs belonging to the splitting group. The second method, which can be used for prediction problems in both regression and classification, builds a non-binary tree in which each split is a binary tree. These two approaches build a maximal tree which is next pruned. To this end, we propose two pruning strategies, one of which is a generalization of the minimal cost-complexity pruning algorithm. Since decisions trees are known to be unstable, we introduce a method of random forests that deals with groups of inputs. In addition to the prediction purpose, these new methods can be also use to perform group variable selection thanks to the introduction of some measures of group importance, This thesis work is supplemented by an independent part in which we consider the unsupervised framework. We introduce a new clustering algorithm. Under some classical regularity and sparsity assumptions, we obtain the rate of convergence of the clustering risk for the proposed alqorithm.

Analog Implicit Functional Testing using Supervised Machine Learning

Bawaskar, Neerja Pramod 27 October 2014 (has links)
Testing analog circuits is more difficult than digital circuits. The reasons for this difficulty include continuous time and amplitude signals, lack of well-accepted testing techniques and time and cost required for its realization. The traditional method for testing analog circuits involves measuring all the performance parameters and comparing the measured parameters with the limits of the data-sheet specifications. Because of the large number of data-sheet specifications, the test generation and application requires long test times and expensive test equipment. This thesis proposes an implicit functional testing technique for analog circuits that can be easily implemented in BIST circuitry. The proposed technique does not require measuring data-sheet performance parameters. To simplify the testing only time domain digital input is required. For each circuit under test (CUT) a cross-covariance signature is computed from the test input and CUT's output. The proposed method requires a training sample of the CUT to be binned to the data-sheet specifications. The binned CUT sample cross-covariance signatures are mapped with a supervised machine learning classifier. For each bin, the classifiers select unique sub-sets of the cross-covariance signature. The trained classifier is then used to bin newly manufactured copies of the CUT. The proposed technique is evaluated on synthetic data generated from the Monte Carlo simulation of the nominal circuit. Results show the machine learning classifier must be chosen to match the imbalanced bin populations common in analog circuit testing. For sample sizes of 700+ and training for individual bins, classifier test escape rates ranged from 1000 DPM to 10,000 DPM.

Constrained clustering and cognitive decline detection /

Lu, Zhengdong. January 2008 (has links)
Thesis (Ph.D.) OGI School of Science & Engineering at OHSU, June 2008. / Includes bibliographical references (leaves 138-145).

Leaf shape recognition via support vector machines with edit distance kernels /

Sinha, Shriprakash. January 1900 (has links)
Thesis (M.S.)--Oregon State University, 2004. / Printout. Includes bibliographical references (leaves 45-46). Also available on the World Wide Web.

Domain knowledge, uncertainty, and parameter constraints

Mao, Yi 24 August 2010 (has links)
No description available.

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